Do not change anything in the following chunk

You will be working on olympic_gymnasts dataset. Do not change the code below:

olympics <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-07-27/olympics.csv')

olympic_gymnasts <- olympics %>% 
  filter(!is.na(age)) %>%             # only keep athletes with known age
  filter(sport == "Gymnastics") %>%   # keep only gymnasts
  mutate(
    medalist = case_when(             # add column for success in medaling
      is.na(medal) ~ FALSE,           # NA values go to FALSE
      !is.na(medal) ~ TRUE            # non-NA values (Gold, Silver, Bronze) go to TRUE
    )
  )

More information about the dataset can be found at

https://github.com/rfordatascience/tidytuesday/blob/master/data/2021/2021-07-27/readme.md

Question 1: Create a subset dataset with the following columns only: name, sex, age, team, year and medalist. Call it df.

df<- olympic_gymnasts|>
  select(name, sex, age,team,year,medalist)
df
## # A tibble: 25,528 × 6
##    name                    sex     age team     year medalist
##    <chr>                   <chr> <dbl> <chr>   <dbl> <lgl>   
##  1 Paavo Johannes Aaltonen M        28 Finland  1948 TRUE    
##  2 Paavo Johannes Aaltonen M        28 Finland  1948 TRUE    
##  3 Paavo Johannes Aaltonen M        28 Finland  1948 FALSE   
##  4 Paavo Johannes Aaltonen M        28 Finland  1948 TRUE    
##  5 Paavo Johannes Aaltonen M        28 Finland  1948 FALSE   
##  6 Paavo Johannes Aaltonen M        28 Finland  1948 FALSE   
##  7 Paavo Johannes Aaltonen M        28 Finland  1948 FALSE   
##  8 Paavo Johannes Aaltonen M        28 Finland  1948 TRUE    
##  9 Paavo Johannes Aaltonen M        32 Finland  1952 FALSE   
## 10 Paavo Johannes Aaltonen M        32 Finland  1952 TRUE    
## # ℹ 25,518 more rows

Question 2: From df create df2 that only have year of 2008 2012, and 2016

df2 <- df|>
filter (year %in% c(2008,2012,2016))
df2
## # A tibble: 2,703 × 6
##    name              sex     age team     year medalist
##    <chr>             <chr> <dbl> <chr>   <dbl> <lgl>   
##  1 Nstor Abad Sanjun M        23 Spain    2016 FALSE   
##  2 Nstor Abad Sanjun M        23 Spain    2016 FALSE   
##  3 Nstor Abad Sanjun M        23 Spain    2016 FALSE   
##  4 Nstor Abad Sanjun M        23 Spain    2016 FALSE   
##  5 Nstor Abad Sanjun M        23 Spain    2016 FALSE   
##  6 Nstor Abad Sanjun M        23 Spain    2016 FALSE   
##  7 Katja Abel        F        25 Germany  2008 FALSE   
##  8 Katja Abel        F        25 Germany  2008 FALSE   
##  9 Katja Abel        F        25 Germany  2008 FALSE   
## 10 Katja Abel        F        25 Germany  2008 FALSE   
## # ℹ 2,693 more rows

Question 3 Group by these three years (2008,2012, and 2016) and summarize the mean of the age in each group.

df2 |>
group_by(year) |>
summarize(
n=n(),
mean_age = mean(age)

)
## # A tibble: 3 × 3
##    year     n mean_age
##   <dbl> <int>    <dbl>
## 1  2008   994     21.6
## 2  2012   848     21.9
## 3  2016   861     22.2

Question 4 Use olympic_gymnasts dataset, group by year, and find the mean of the age for each year, call this dataset oly_year. (optional after creating the dataset, find the minimum average age)

oly_year <- olympic_gymnasts |>
group_by(year) |>
summarize(
n=n(),
mean_age = mean(age),
)
oly_year
## # A tibble: 29 × 3
##     year     n mean_age
##    <dbl> <int>    <dbl>
##  1  1896    73     24.3
##  2  1900    33     22.2
##  3  1904   317     25.1
##  4  1906    70     24.7
##  5  1908   240     23.2
##  6  1912   310     24.2
##  7  1920   206     26.7
##  8  1924   499     27.6
##  9  1928   561     25.6
## 10  1932   140     23.9
## # ℹ 19 more rows
min(oly_year$mean_age)
## [1] 19.86606

Question 5 This question is open ended. Create a question that requires you to use at least two verbs. Create a code that answers your question. Then below the chunk, reflect on your question choice and coding procedure

# Your R code here
df3 <- olympic_gymnasts |>
select(team,year,medalist)
df3
## # A tibble: 25,528 × 3
##    team     year medalist
##    <chr>   <dbl> <lgl>   
##  1 Finland  1948 TRUE    
##  2 Finland  1948 TRUE    
##  3 Finland  1948 FALSE   
##  4 Finland  1948 TRUE    
##  5 Finland  1948 FALSE   
##  6 Finland  1948 FALSE   
##  7 Finland  1948 FALSE   
##  8 Finland  1948 TRUE    
##  9 Finland  1952 FALSE   
## 10 Finland  1952 TRUE    
## # ℹ 25,518 more rows
df4<- df3|>
filter(team %in% c( "Finland","Norway","Romania")) |>
filter(year %in% c(1920,1948,2000)) |>
filter (medalist) |>
count (team,year, name = "medalist")
df4
## # A tibble: 3 × 3
##   team     year medalist
##   <chr>   <dbl>    <int>
## 1 Finland  1948       17
## 2 Norway   1920       25
## 3 Romania  2000       11

Discussion: Enter your discussion of results here.

For this question, I wanted to explore how many gymnasts won medals from Finland,Norway,and Romania in three different Olympic year:1920,1948,and 2000.

First, I used select to focus only on team,year,and medalist and I named that data frame as df3. Then, I used filter three times for get the teams,year I wanted. And keep only the medalists. Finally, I used count to summarize how many medalists each team had in each year.And I named that data frame as df4.

This showed me that Finland had 17 medalists in 1948,Norway had 25 in 1920, and Romania had 11 in 2000. I was surprised because like 2000 only has medalists from Romania ,1920 only has medalists from Norway , and 1948 has medalists from Finland.